Analysis of Brain Tissue and Cerebrospinal Fluid Feature for Alzheimer's Disease Detection

Cucun Very Angkoso; I. Ketut Eddy Purnama; Mauridhi Hery Purnomo

doi:10.1109/CENIM.2018.8711088

Analysis of Brain Tissue and Cerebrospinal Fluid Feature for Alzheimer's Disease Detection

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder which connected to the progression of declining in memory and thinking skills. A more accurate diagnosis and appropriate management are needed to determine correct treatments. Human brain imagery using Magnetic Resonance Imaging (MRI) has different kinds of brain tissue: Grey-Matter (GM) and White Matter (WM), and image of colorless body fluid which called Cerebrospinal Fluid (CSF). This article discusses the use of GM, WM and CSF imagery and the proportion values of the volume of each image in determining Alzheimer's identification. Our study is using 3different anatomical plane image acquisition of MRI and using feature extraction based on Kolmogorov-Smirnov distance. Supervised Neural Network Backpropagation was used for classifier machine. The results of testing on every combination of the different anatomical plane of MRI images we finally proposed that individual WM or GM analyses are a better compared to the scenario of combining between GM, WM, and CSF.

Original language	English
Title of host publication	2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	285-288
Number of pages	4
ISBN (Electronic)	9781538675090
DOIs	https://doi.org/10.1109/CENIM.2018.8711088
Publication status	Published - 2 Jul 2018
Event	2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Surabaya, Indonesia Duration: 26 Nov 2018 → 27 Nov 2018

Publication series

Name	2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding

Conference

Conference	2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018
Country/Territory	Indonesia
City	Surabaya
Period	26/11/18 → 27/11/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Alzheimer's
Kolmogorov-Smirnov distance
cerebrospinal fluid
grey matter
neural network backpropagation
white matter

Access to Document

10.1109/CENIM.2018.8711088

Cite this

Angkoso, C. V., Purnama, I. K. E., & Purnomo, M. H. (2018). Analysis of Brain Tissue and Cerebrospinal Fluid Feature for Alzheimer's Disease Detection. In 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding (pp. 285-288). Article 8711088 (2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CENIM.2018.8711088

Angkoso, Cucun Very ; Purnama, I. Ketut Eddy ; Purnomo, Mauridhi Hery. / Analysis of Brain Tissue and Cerebrospinal Fluid Feature for Alzheimer's Disease Detection. 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 285-288 (2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding).

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title = "Analysis of Brain Tissue and Cerebrospinal Fluid Feature for Alzheimer's Disease Detection",

abstract = "Alzheimer's disease (AD) is a progressive neurodegenerative disorder which connected to the progression of declining in memory and thinking skills. A more accurate diagnosis and appropriate management are needed to determine correct treatments. Human brain imagery using Magnetic Resonance Imaging (MRI) has different kinds of brain tissue: Grey-Matter (GM) and White Matter (WM), and image of colorless body fluid which called Cerebrospinal Fluid (CSF). This article discusses the use of GM, WM and CSF imagery and the proportion values of the volume of each image in determining Alzheimer's identification. Our study is using 3different anatomical plane image acquisition of MRI and using feature extraction based on Kolmogorov-Smirnov distance. Supervised Neural Network Backpropagation was used for classifier machine. The results of testing on every combination of the different anatomical plane of MRI images we finally proposed that individual WM or GM analyses are a better compared to the scenario of combining between GM, WM, and CSF.",

keywords = "Alzheimer's, Kolmogorov-Smirnov distance, cerebrospinal fluid, grey matter, neural network backpropagation, white matter",

author = "Angkoso, \{Cucun Very\} and Purnama, \{I. Ketut Eddy\} and Purnomo, \{Mauridhi Hery\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 ; Conference date: 26-11-2018 Through 27-11-2018",

year = "2018",

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doi = "10.1109/CENIM.2018.8711088",

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Angkoso, CV, Purnama, IKE & Purnomo, MH 2018, Analysis of Brain Tissue and Cerebrospinal Fluid Feature for Alzheimer's Disease Detection. in 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding., 8711088, 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding, Institute of Electrical and Electronics Engineers Inc., pp. 285-288, 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018, Surabaya, Indonesia, 26/11/18. https://doi.org/10.1109/CENIM.2018.8711088

Analysis of Brain Tissue and Cerebrospinal Fluid Feature for Alzheimer's Disease Detection. / Angkoso, Cucun Very; Purnama, I. Ketut Eddy ; Purnomo, Mauridhi Hery.
2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2018. p. 285-288 8711088 (2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Analysis of Brain Tissue and Cerebrospinal Fluid Feature for Alzheimer's Disease Detection

AU - Angkoso, Cucun Very

AU - Purnama, I. Ketut Eddy

AU - Purnomo, Mauridhi Hery

PY - 2018/7/2

Y1 - 2018/7/2

N2 - Alzheimer's disease (AD) is a progressive neurodegenerative disorder which connected to the progression of declining in memory and thinking skills. A more accurate diagnosis and appropriate management are needed to determine correct treatments. Human brain imagery using Magnetic Resonance Imaging (MRI) has different kinds of brain tissue: Grey-Matter (GM) and White Matter (WM), and image of colorless body fluid which called Cerebrospinal Fluid (CSF). This article discusses the use of GM, WM and CSF imagery and the proportion values of the volume of each image in determining Alzheimer's identification. Our study is using 3different anatomical plane image acquisition of MRI and using feature extraction based on Kolmogorov-Smirnov distance. Supervised Neural Network Backpropagation was used for classifier machine. The results of testing on every combination of the different anatomical plane of MRI images we finally proposed that individual WM or GM analyses are a better compared to the scenario of combining between GM, WM, and CSF.

AB - Alzheimer's disease (AD) is a progressive neurodegenerative disorder which connected to the progression of declining in memory and thinking skills. A more accurate diagnosis and appropriate management are needed to determine correct treatments. Human brain imagery using Magnetic Resonance Imaging (MRI) has different kinds of brain tissue: Grey-Matter (GM) and White Matter (WM), and image of colorless body fluid which called Cerebrospinal Fluid (CSF). This article discusses the use of GM, WM and CSF imagery and the proportion values of the volume of each image in determining Alzheimer's identification. Our study is using 3different anatomical plane image acquisition of MRI and using feature extraction based on Kolmogorov-Smirnov distance. Supervised Neural Network Backpropagation was used for classifier machine. The results of testing on every combination of the different anatomical plane of MRI images we finally proposed that individual WM or GM analyses are a better compared to the scenario of combining between GM, WM, and CSF.

KW - Alzheimer's

KW - Kolmogorov-Smirnov distance

KW - cerebrospinal fluid

KW - grey matter

KW - neural network backpropagation

KW - white matter

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DO - 10.1109/CENIM.2018.8711088

M3 - Conference contribution

AN - SCOPUS:85066471177

T3 - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding

SP - 285

EP - 288

BT - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018

Y2 - 26 November 2018 through 27 November 2018

ER -

Angkoso CV, Purnama IKE , Purnomo MH. Analysis of Brain Tissue and Cerebrospinal Fluid Feature for Alzheimer's Disease Detection. In 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding. Institute of Electrical and Electronics Engineers Inc. 2018. p. 285-288. 8711088. (2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding). doi: 10.1109/CENIM.2018.8711088

Analysis of Brain Tissue and Cerebrospinal Fluid Feature for Alzheimer's Disease Detection

Abstract

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UN SDGs

Keywords

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